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Journal of Cleaner Production 129 (2016) 608e621

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Journal of Cleaner Production

journal homepage: www.elsevier .com/locate/ jc lepro

Critical success factors for reverse logistics in Indian industries: a structural model

Sachin Kumar Mangla a, Kannan Govindan b, *, Sunil Luthra c

a Department of Mechanical Engineering, Graphic Era University, Dehradun, India b Center for Sustainable Engineering Operations Management, Department of Technology and Innovation, University of Southern Denmark, Denmark c Department of Mechanical Engineering, Government Polytechnic, Jhajjar, Haryana, India

a r t i c l e i n f o

Article history: Received 22 February 2015 Received in revised form 22 February 2016 Accepted 16 March 2016 Available online 12 April 2016

Keywords: Reverse logistics (RL) Critical success factors (CSFs) Sustainability AHP DEMATEL Indian manufacturing industries

* Corresponding author. E-mail address: [email protected] (K. Govindan).

http://dx.doi.org/10.1016/j.jclepro.2016.03.124 0959-6526/© 2016 Elsevier Ltd. All rights reserved.

a b s t r a c t

Industries face significant pressures to enact eco-friendly practices in their supply chain due to the constraints of natural resources and growing ecological awareness among customers. Reverse logistics (RL) has been considered as a systematic approach for industries to improve their environmental impacts and to ensure sustainability in business. Industries are enthusiastic to adopt RL activities in their busi- nesses, but they also face challenges such as insufficient knowledge and resources regarding RL imple- mentation. Therefore, we seek to evaluate the critical success factors (CSFs) linked to the implementation of RL in manufacturing industries in India. In this work, a structural model is proposed by using Analytical Hierarchy Process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) methods to evaluate the CSFs in RL adoption. The AHP methodology assists in establishing the priorities of the CSFs, while the DEMATEL approach categorizes the causal relationships among them. The findings of this work shows that the Global competitiveness main factor is highly prioritized, and thus, needs to be focused greatly in order to increase the effectiveness of RL adoption in business. The relative priority of the remaining main factors through AHP analysis is given as Regulatory factors - HR and organizational factors -Economic factors - Strategic factors. The findings also indicate that Global competitiveness; Regulatory; HR and organizational main factors are classified under cause group, while Economic and Strategic main factors belong to effect group. This model will help business analysts and supply chain managers formulate both short-term and long-term, flexible decision strategies for successfully man- aging and implementing RL adoption in the supply chain scenarios.

© 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Conservation of the environment has taken a prime position among areas of concern for managers and practitioners all across the globe. Likewise, customers are more environmentally conscious, which creates a demand for industries to adopt clean, green, eco-friendly processes for their businesses (Millet, 2011; Sarkis et al., 2011; Almeida et al., 2013; Seuring and Gold, 2013; Luthra et al., 2014a, 2015a; Gandhi et al., 2015). Growing competitive and financial pressures, diminishing product life cy- cles, and stringent environmental rules have increased the attention paid to green and Reverse Logistics (RL) activities that industries embrace to improve their environmental impacts

(Subramoniam et al., 2009; Chan et al., 2012; Mangla et al., 2013; Zhu and Geng, 2013). RL comprises all operations related to the recovery and reuse of products and materials, and proves to be a rational instrument for industries to improve their firms' sus- tainability in terms of ecological, economic, and social gains (Schwartz, 2000; Sarkis, 2003, 2010; Zhang et al., 2011; Nikolaou et al., 2013; Abdulrahman et al., 2014). In addition, RL opera- tions and its related practices are also proven to be crucial in reducing operational expenses (PricewaterhouseCoopers' report, 2008). RL has gained attention among business organizations as an effective, strategic approach to improving profitability, product lifecycles, supply chain complexity, consumer preferences, and reducing environmental impact (Thierry et al., 1995; Fleischmann et al., 1997; Carter and Ellram, 1998; Van Hoek, 1999; Stock, 1998, 2001; Toffel, 2003; Neto et al., 2008; Tsai et al., 2009; Hu and Bidanda, 2009; Gunasekaran and Spalanzani, 2012; Govindan et al., 2015).

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However, the adoption and implementation of RL practices is relatively difficult from industrial viewpoints. Many industries are comparatively less familiar with how to initiate RL and what ben- efits could be realized through implementing RL practices (Chan and Kai Chan, 2008). To deal with this uncertainty, scholars and practitioners have tried to isolate the important determinants of initiation and implementation of RL among industries (Vijayan et al., 2014).

Several factors are vital to the successful implementation of RL in business, such as management commitment, globalization, reg- ulations, consumer requirements, financial resources, competi- tiveness, and benchmarking (Jindal and Sangwan, 2011; Chio et al., 2012; Mangla et al., 2013). Given that these factors are critical for industries in order to adopt RL efficiently (Chio et al., 2012), we need to identify and evaluate the various Critical Success Factors (CSFs) required for the implementation of RL practices in the in- dustrial supply chain.

The goal of this work is to evaluate the CSFs related to initiation and implementation of RL on tactical (or operational) and strategic levels in business. It is no surprise that different industries may exhibit different perceptions in adopting RL practices in their respective businesses (Srivastava and Srivastava, 2006). To acknowledge these considerations and to achieve the above formulated objectives, a two phase-methodology is introduced and used in this work. In the first phase, various CSFs that assist in the implementation of RL from the industrial viewpoint are deter- mined. For this phase, several different industries operational in the western region of India were examined. A literature survey and discussions from industrial experts resulted in a collection of the most commonly accepted RL implementation CSFs. In the second phase, the finalized common RL implementation CSFs were sub- jected to evaluation, using Analytical Hierarchy Process (AHP) and Decision Making Trial and Evaluation Laboratory (DEMATEL) methods, through the input of industry and field experts. The AHP method (Saaty, 1980) helps to prioritize or to identify the essential RL implementation CSFs. On the other hand, the DEMATEL method (Gabus and Fontela, 1972) is used to study the interrelationships between the RL implementation CSFs with the help of a causal map. It assists practicing managers and policy makers to prepare both short-term and long-term flexible decision strategies that will prove beneficial for performance improvements of RL imple- mentation from an industrial perspective.

The remainder of the paper is arranged as follows. Literature relevant to this work is discussed in Section 2. Section 3 provides detail on the proposed research methods. The proposed research framework is given in Section 4, and its application to Indian manufacturing industries is presented in Section 5. Next, Section 6 identifies results obtained from the research and their implications. Finally, research conclusions are presented in Section 7, along with limitations and scope for future work.

2. Relevant literature

The present section includes the literature on RL implementa- tion in industries in Indian context, RL implementation factors, and draws the research gaps for this study.

2.1. Industrial RL implementation in India

RL can be expressed as the process of planning, implementing, and regulating the efficient and cost effective flow of rawmaterials, in-process inventory, finished goods, and related information from the point of consumption to the point of origin for the purpose of recapturing value or proper disposal (Rogers and Tibben-Lembke, 2001; De Brito and Dekker, 2004; Blumberg, 2005; Meade et al.,

2007; Wadhwa et al., 2009). With regard to the adoption and implementation of RL initiatives, Abdulrahman et al. (2014) argued that RL literature in developing countries context is still in its in- fancy state. India accounts for approximately 17.5% of the world's population. Due to industrialization, manufacturing industries are growing at a rapid pace, leading to the generation of a huge amount of hazardous and non-hazardous waste. According to Comptroller and Auditor- General's (CAG) report, over 7.2 MT of industrial or hazardous waste was generated in India in 2000, out of which 1.4 million tonswas recyclable, 0.1 million tons was incinerateable, and 5.2 million tons was destined for disposal on land (MoEF, 2000). In addition, India Central Pollution Control Board (CPCB, 2000) documented that some 41,523 industries in the country generate about 7.90 million tons of hazardous/industrial waste every year, of which recyclable hazardous waste is 3.98 million tons (50.38%), landfill waste is 3.32 million tons (42.02%), and incinerateable waste is 0.60 million tons (7.60%). The waste sector in India has evolved greatly in last 15 years (from 2000 onwards) and waste is generated in several forms such as industrial waste, e-waste, and bio medical waste, municipal waste. According to the report of Novonous waste management market in India is expected to be worth US$ 13.62 billion by 2025. It is expected that. E-waste management market is likely to grow at a compound annual growth rate (CAGR) of 10.03% by 2025. Bio medical waste man- agement market may grow at a CAGR of 8.41% (Novonous, 2014). In addition, the generation andmanagement of Municipal SolidWaste (MSW) is also becoming a serious problem for India. Indian MSW management market is likely to grow at a CAGR of 7.14% by 2025. Based on the latest report of CPCB (CPCB, 2014) 1, 27,486 Tons per day (TPD) of MSW was generated in India in 2011e12. Of which, only 15,881 TPD (12.50%) was processed for eco-friendly disposal (MoEF, 2012e13). Recently, Kannan et al. (2016) suggested that formal recycling of e-waste could contribute to sustainable society.

From these numbers, we can conclude that there is a substantial scope of RL implementation within Indian industries that, if implemented, would be crucial not only in reducing the amount of waste but also in improving organizational, ecological, financial, and competitive performance levels (MoEF, 2012e13).

RL is distinguished as a crucial means to lower the waste gen- eration and to prevent pollution by managing the environmental burden of products after their end-of-life (Ravi, 2012). However, the concept of RL is not as popular among Indian business organiza- tions (Ravi et al., 2005; Hung Lau and Wang, 2009; Sharma et al., 2011). In India, this hesitance may be due to lack of support from top management and other business partners; these decision makers are often not ready to spend more money to implement RL solutions after investing large amounts of capital to set up the fa- cility and infrastructure (Ravi et al., 2005). Also, governmental support has an influence on the strategic decision of RL imple- mentation for any organization. Analyzing some RL studies relevant to Indian contexts, Jindal and Sangwan (2011) listed and analyzed sixteen barriers to the implementation of RL through their litera- ture studies. They find that RL practices can play an important role in achieving sustainability in Indian business contexts. In their study, Sharma et al. (2011) analyzed barriers in context to Indian industries for RL implementation and segregated factors into driving factors and driven factors. Srivastava and Srivastava (2006) examined several categories of products in order to make a sys- tematic understanding of the possibility of implementing RL in Indian context. Ravi et al. (2005) described the management of RL operations by investigating a paper industry. Pati et al. (2008) presented a mixed integer goal programming model to help in the appropriate management of the RL system through paper recycling in India. Govindan et al. (2012) analyzed third party RL providers with the help of interpretive structural modeling by

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621610

taking a case study from a tire company. Diabat et al. (2013) examined the interaction among major barriers hindering the implementation of third-party logistics in Indian manufacturing industries. Lack of qualification for employees in third-party lo- gistics provider and fear of employees of the firm have been found as the most hindering barriers in implementation of third-party logistics. Mangla et al. (2013) recognized and analyzed fourteen variables related to the handling and returning of products by closing the loop of a green focused supply chain in paper mill in- dustries in an Indian perspective. Researchers have determined that the different variables associated with the initiation and or implementation of product recovery activities (i.e., RL initiatives) are important to distinguish, and their subsequent analysis may help the decision makers to achieve higher ecological-economic benefits (Mangla et al., 2012).

In addition, in the 12th Five Years Plan (2012e2017), it appears that RL is being practiced in India, but it is still in unorganized sectors and not much consideration is given to improving envi- ronmental performances. Under these considerations, Critical Success Factors (CSFs) of RL implementation need to be identified and analyzed more rigorously. This step would help industries in India to implement RL in their respective businesses, and to approach RL in a more organized chain. It will further assist Indian industries to improve their economical, social, and environmental performances, and it should strengthen sustainability in business (Jindal and Sangwan, 2011).

2.2. RL implementation factors

Gonz�alez-Benito and Gonz�alez-Benito (2006) confirmed that pressure from stakeholders and the values and beliefs endorsed by the manager's environmental awareness leads more quickly to the implementation of eco-friendly practices in logistics operations. It also reveals the fact that the organizations with environmentally aware managers tend not to follow a reactive approach; instead, they are more proactive towards eco-friendly requirements. How- ever, in accordance with the study conducted by Chio et al. (2012), the successful implementation of RL leads to improvisation in the organization's performance, financial position, and competitive advantage. In the same work, these authors also insist that a suc- cessful implementation of RL is only possiblewith topmanagement support and commitments. The foremost requirement of all is the integration of every function for a smooth flow of material in both (forward and reverse) directions.

Nevertheless, there are several external and internal factors governing the effective and efficient implementation of RL prac- tices in the supply chain. Some of the external and internal factors suggested by researchers are government regulations, customer demand, policy entrepreneurs, support of top management, stakeholder commitment, incentive systems, quality of inputs, and vertical integration (Srivastava, 2008; Hung Lau and Wang, 2009; Tsai et al., 2009; Rahman and Subramanian, 2012; Dowlatshahi, 2012). Ho et al. (2012) concluded that internal and external fac- tors significantly influence RL. They suggest that financial and hu- man resources play an important role in companies' implementation of RL, whereas tangible resources do not have much influence on the practice. They also declare that companies with excellent collaboration and relationship with other business partners can make use of RL more effectively and efficiently (Ho et al., 2012). Rogers and Tibben-Lembke (1999) identified several key RL management elements, including asset recovery, compact- ing disposition cycle time, centralized return centers, gate keeping, zero returns, negotiation, RL information systems, remanufacture and refurbishment, financial management, and outsourcing. Carter

and Ellram (1998) listed some critical RL implementation factors given as regulations, customer demand, policy entrepreneurs, and so forth.

It has been stated that the critical (key) success factor theory enables managers to know the importance of process improvement for their company (Grimm et al., 2014). The theory of critical suc- cess factors is primarily based on strategy research, which recog- nizes the functions, activities, and measures to improve a company's competitive advantage from an organizational supply chain context (Dinter, 2013; Vasconcellos and S�a, 1988). Hence, it is important to align Critical Success Factors (CSFs) with the firm's desired outcome. However, constant supervision is required to recognize CSF and its relevant activities to support decision making and to develop high performance management systems, especially in supply chains (Bai and Sarkis, 2012). Therefore, the identification of CSF in terms of both how and why is important steps in adopting and implementing RL initiatives from a supply chain context.

2.3. Research gaps

The benefits of RL implementation are not yet fully realized in some of the world's emerging economies. The adoption and implementation of RL practices is also relatively difficult frommany industrial viewpoints (Prakash and Brua, 2015). While a lot of attention has been paid to the implementation of RL practices in developed countries, there is still a lot to do in a developing country like India (Jindal and Sangwan, 2011; Sharma et al., 2011; Subramanian et al., 2014). Govindan et al. (2015) suggested in their research that multi objective decision making is still a gap in different studies as compared to single objective analyses in the area of RL/CLSC. As real world problems are rarely single objective only, it is necessary for researchers to pay more attention to multi objective functions instead of single objective ones (Govindan et al., 2015).

From the extensive literature, we observed that several enablers and barriers exist to implement RL activities in the business (Jindal and Sangwan, 2011; Chio et al., 2012; Bouzon et al., 2016). To the best of our knowledge, the specific consideration of CSFs in the implementation of reverse logistics to maximize sustainable ad- vantages is not covered in the literature. Business organizations face many complexities and challenges in implementing RL activities.

Thus, this work aims to identify the RL implementation CSFs to provide a theoretical ground for the managers by showing the role of identified CSFs in RL implementation initiatives. The identified CSFs can help in understanding the realistic issues to adopting RL practices from an organizational supply chain perspective. Hence, within the framework and understanding of the theory of CSFs, the present research seeks to identify and analyze CSFs to contribute to successful implementation of reverse logistics from the Indian manufacturing industry perspective. Tomeet the above highlighted research gap, the AHP and DEMATEL methods have been used; other details about the application of AHP and DEMATEL are given in next section.

3. Research methods

This section presents the description of the proposed and uti- lized research methods. The AHP method has been used to rank factors according to their significance on the basis of industry ex- perts' opinion. However, there is a need to determine the causal interactions between factors useful for managers in framing short- term decision making strategies (Najmi and Makui, 2010). DEMA- TEL is recognized as a powerful tool in dealing with the issue; it portrays a basic concept of contextual relation among the elements

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of the system. The DEMATEL method can evaluate decision ele- ments by signifying the interdependence between them, which may help policy makers to frame long-term decision strategies (Chio et al., 2012). Thus, the AHP and the DEMATEL methods when applied together will give a clearer illustration of use for industries to plan both the tactical or operational and the strategic decision strategies. However, the details of the research methods are given in the following sub-sections.

3.1. AHP method

AHP, first introduced by Thomas L. Saaty (1980), is a flexible multi criteria decision analysis technique, designed to solve un- structured decision problems. The AHP technique is based on the fundamentals of the decomposition of the problem, of the pair- wise assessments, and finally of the generation and synthesis of priority vector (Ho, 2008; Sarmiento and Thomas, 2010; Luthra et al., 2015b). In contrast to the analytic network process (ANP), AHP is a linear evaluation technique. On the other hand, it needs to develop several pair-wise assessment matrices in ANP, and in addition, it involves a complex survey process for non-expert's viewpoint (Harputlugil et al., 2011). The methodology of AHP en- ables the managers to analyze the complicated system more easily (Vaidya and Kumar 2006; Talib et al., 2011; Govindan et al., 2014; Mani et al., 2014; Kumar et al., 2015; Mangla et al., 2015c). How- ever, AHP has several limitations as well, given as (Ishizaka and Labib, 2009):

� Rank reversal (i.e. changes in the importance ratings whenever criteria or alternatives are added-to or deleted-from the initial set of criteria or alternatives compared).

� The assumption of criteria independence. � The use of judgment scales whilemaking pair-wise comparisons may involve ambiguity and human bias.

The steps involved in employing the AHP methodology (Chang et al., 2007; Madaan and Mangla, 2015) for this research are described as below:

Step 1: To define the goal: The goal of this research, i.e. to evaluate the success factors in implementation of RL, is defined. Based on this, the factors and sub-factors are established that help in structuring a decision hierarchy. The sources of literature and expert judgments will be crucial for this. Step 2: To collect data and form the pair-wise evaluations: In this step, data is collected to frame the pair-wise evaluations among factors. A judgment matrix (designated as ‘A’) is formed which is used for calculating factor priorities. Let A1, A2 … An, be the set of stimuli. The computed judgments on a pair of stimuli Ai, Aj, are denoted as,

A ¼ �aij �

where; i; j ¼ 1;2;&;n: (1) The survey instrument in terms of questioners' evaluation can

be used to collect data. Based on the data collected, the rating or pair-wise evaluations among the factors are acquired by means of a nine rating Saaty's scale, which assists to achieve numerical quantities representing the values of aij (elements of the pair-wise comparison matrix) transformed from verbal judgments.

Step 3: To attain the Eigen values and Eigen vectors: In this step, the framed pair-wise evaluation matrices were operated in or- der to obtain the importance weights of the factors. Based on obtained importance weights, the priority for the respective factor is attained.

3.2. DEMATEL method

DEMATEL approach was developed by Science and Human Af- fairs Program of the Battelle Memorial Institute of Geneva some- where in 1972 and 1976 (Gandhi et al., 2015). This method relies on graph theory, and enables an analysis of complicated problems by means of visualization techniques (Lin, 2013). Compared to inter- pretive structural modeling (ISM), the methodology of DEMATEL, on the other hand, assists in capturing the contextual relations be- tween elements in the system and defining the strength of their interrelationships, as well (Wu, 2008). The procedural steps of DEMATEL methodology (Tzeng and Huang, 2011; Jia et al., 2015) with regard to this work is given as follows:

Step 1: To define the goal and factors to be evaluated: In this step, a critical review of literature is required to explore and gather relevant data. The expert's judgment is also crucial in this step for discussion on the issue to achieve the goal. The probable factors associated with the effective implementation of RL are selected and finalized as factors to be evaluated from the in- formation gathered and expert judgments. Step 2: To form the initial direct relation matrix and average matrix (M): An initial relation matrix is formulated based on the direct influence between any two factors and is obtained through the expert's judgment by asking them to score the factor on the basis of scale given as, 0e ‘No influence’; 1e ‘Little influence’; 2 e ‘High influence’; 3 e ‘Very high influence’.

If ‘n’ be the number of factors and ‘k’ be the number of re- spondents with 1 � k � H, then for each respondent (n � n) non- negative matrices can be established as Xk ¼ [xkij]. The notation ‘xij’ indicates the degree to which the expert conceives that factor i affects factor j. Based on this, it can be possible to construct X1, X2, X3 …, XH matrices given by H respondents respectively (H repre- sents the number of experts). To incorporate all opinions from H respondents, the average matrix or the average direct relation matrix A ¼ [aij] is constructed by means of Eq. as follows:

mij ¼ 1 H

XH

K¼1 xkij: (2)

Step 3: To compute the normalized direct-relation matrix (D): The average matrix (M) is transformed into a normalized direct- relation matrix by using the Eq. given below,

D ¼ M � S (3)

where, S ¼ min

2 6664

1 max

Pn j¼1jmijj

; 1 max

Pn i¼1jmijj

3 7775 .

Step 4: To attain the total relation matrix (T): The total relation matrix (T) is computed by using the Eq. given below:

T ¼ DðI � DÞ�1 (4)

where ‘I’ is the identity matrix, after attaining the Matrix T ¼ [tij]n�n, the summation of all the rows and columns are calculated.

Let [ri]n�1 and [cj]1�n be the vectors representing the sum of rows and sum of columns of the total relationmatrix respectively. ri

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621612

summarizes both direct and indirect effects imparted by factor ‘i’ to the other factors, whereas, cj depicts both direct and indirect effects received by factor ‘j’ from the other factors. Sum (ri þ cj) known as ‘Prominence’ demonstrates the total effects given and received by factor ‘i’, whereas the difference (ri - cj) known as ‘Relation’ dem- onstrates the net effect through which factor ‘i’ impacts the system. Specifically, if the value (ri - cj) is positive, factor ‘i’ is in the net cause group, while factor ‘i’ will be in the net receiver group if the value (ri - cj) is negative (Tzeng et al., 2007).

4. Proposed research framework

The research framework for evaluating the CSFs in effective adoption and implementation of RL practices, based on the AHP and DEMATEL methods, consists of three phases. Phase 1: identi- fication of the most common RL implementation CSFs from litera- ture resources and from industrial and field expert inputs. Phase 2: prioritizing the CSFs to develop the short-term, flexible decision plans in order to adopt RL practices using the AHPmethod. Phase 3: analyzing the causal interactions among CSFs to formulate the long-term, flexible decision strategies in order to adopt RL practices using the DEMATEL method. The research framework for evalu- ating the CSFs in implementation of RL in Indian manufacturing industries is shown in Fig. 1.

5. An application example of the proposed model to manufacturing industries in India

5.1. Data collection

The main source of the data collection is manufacturing com- panies operational in the western region of India. A total of 50 manufacturing companies were targeted for the data collection. These companies were covered under convenience sampling, not

Fig. 1. Proposed Rese

random sampling. Companies were selected on the basis of prior experience and using personal contacts. There is no formula for taking sample size in convenience sampling. It all depends upon the on cost and resources needed for data analysis and time limits to complete the project. Due to cost and resources and time con- straints, it is assumed that the considered sample size would be sufficient and representative of the population under analysis.

Further, after frequent phone calls, e-mails and meetings, 42 companies agreed to take part in the process in the end. A ques- tionnaire was formed and circulated among various middle and senior level managers and field experts of the manufacturing companies in question to collect data needed for this research work. The selected managerial and field experts are highly profi- cient in their respective domains and have an industrial experience of more than 10 years. The middle and lower level managers were primarily selected for data collection, because they are primarily involved in strategic decision making of adoption and imple- mentation of RL initiatives from the industrial context (Mangla et al., 2015a). After having several discussion sessions and group meetings with experts, a total of 42 replies were collected. Out of these 42 replies, 30 replies were found suitable in all respects (i.e. completely filled). These 30 replies were examined for further analysis. The response rate was nearly around 60%, which is acceptable. Further, according to Malhotra and Grover (1998), a response rate of above 20% is considered as a reasonable one. The basic profile of the respondent industries is shown in Table 1.

The data collected is used in three phases as described in the following sub-sections.

5.1.1. Phase 1: identification and selection of the common RL implementation critical success factors

Initially twenty-two CSFs for the implementation of RL were identified on the basis of literature review. Later, a questionnaire was formed and mailed to different manufacturing industries in

arch framework.

Table 1 Basic profile of the respondent industries.

S. No. Basic data of respondents Criteria Number of respondent

1 Type of industry Paper industry 14 Sugar industry 05 Heavy engineering 05 Automobile industry 14 Iron and steel industry 04 Total 42

2 Annual turnover (in Indian rupees)

Less than or Equal to 1000 Millions

15

1001 to 5000 Millions 20 More than 5000 Millions 07 Total 42

3 Nature of business Original Equipment Manufacturer

06

Supplier 36 Total 42

4 Average numbers of suppliers

Less than or Equal to 50 04 50 to 200 20 More than 200 18 Total 42

5 Environmental management system

Yes 30 No 0 In Progress 12 Total 42

Source: Industry log book, data records, and expert inputs.

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India for their inputs to rank the significance of each factor on the scale of 1e5 (where, 1-least significant, 2- less significant, 3- sig- nificant, 4- high significant, and 5-most significant). The purpose of this scalewas to rate the importance of initially identified 22 factors with regard to the discussion sessions arranged with the experts according to the effective adoption and implementation of RL ini- tiatives in Indian context. The factors with a rating of 1 or 2 were decided to be deleted, but we retained ratings of 3 and above for each factor. Therefore, there was no elimination from the initial list. In addition, a column was added to the questionnaire where the respondents (industry and field experts) can add any other critical factor important to the RL implementation point of view as per their perception. By virtue of this, 3 more factors were added to the initial literature identified 22 RL implementation CSFs. The three added factors are given as e Extended producer responsibilities, Benchmarking, and Globalization. Hence, a total of 25 CSFs linked to implementation of RL for Indian manufacturing industries are selected. These finalized 25 CSFs were then classified into five main factors depending on their intended meaning and functional sim- ilarities (see Table 2). These are given ase Regulatory factors (RF), Global competitiveness factors (GCF), Economic factors (EF), HR and organizational factors (HROF), and Strategic factors (SF).

5.1.2. Phase 2: determining the relative importance of the RL implementation common success factors using AHP

The finalized common RL implementation CSFs is evaluated by using the AHPmethod. It helps to recognize the relative importance of each factor based on the ranks obtained from their numerical priorities. For this, a hierarchal structure is constructed to analyze the problem. It comprises of three levels: goal statement (Level-1), main factors (Level-2), and sub factors (Level-3) as shown in Fig. 2.

Next, the pair-wise evaluation matrix for the main factors and each sub factor is constructed by taking into consideration the expert's judgments. The importance rating of each expert is collected based on scale as mentioned in Section 3.1. Notably, the geometric meanmethod is among themost common usedmethods in AHP to aggregate the individual ratings of the experts (Saaty, 2008). Thus, in this work, geometric mean of individual opinions

is computed for determining the ranks of the factors. The pair-wise evaluation matrix for the main group factors is represented in Table 3 below.

After following the steps mentioned in Section 3.1, Eigen values and Eigen vectors are calculated, and is given as maximum Eigen value ¼ 5.245; Consistency index (C.I.) ¼ 0.0612. The relative weights attained and corresponding ranks for the main factor are shown in Table 4.

The consistency ratio (C.R.) is calculated which comes out to be 0.055 (C.R. ¼ 0.0612/1.11). As evident, the consistency ratio (C.R.) is well below the permissible limits (i.e. C.R.� 0.10); thus, the results are considered to be acceptable. Likewise, the relative weights of all the sub factors are calculated. Further, for obtaining the global weights of all the sub factors, the relative weight of each main factor is multiplied with its corresponding sub factor weights (see Table 5).

5.1.3. Phase 3: determining interdependence among the RL implementation common success factors using DEMATEL

In this phase, DEMATEL approach is used in order to determine the interdependence between listed common CSFs relevant to RL implementation from industries in Indian perspective. It assists to evaluate the interrelationship between the CSFs in terms of a causal effect map. For this, the same selected industry and field experts were contacted and asked to rate the CSFs on the scale of 0e3 depending upon the influence of one factor over other factors. This step is done to construct the pair-wise matrix of the main success factors needed to construct the average matrix (A) and which is formed by taking the average of the responses of the experts (shown in Table 6).

In the next step, the normalized initial direct relation matrix (D) is formed using Eq. (3) (see Table 7).

Following this, the total relation matrix (T) is constructed by using Eq. (4), and is shown in Table 8.

According to Table 8, values in (r þ c) column (i.e. prominence), demonstrates the total effect of each main factor over the entire system; thus, Global competition factors (GCF) havemore influence in comparison to other success factors. Likewise, values in (r e c) column (i.e. relation), helps to divide the success factors into cause and effect groups depending on their positive and negative values attained respectively. Next to this, the threshold value has been calculated, which facilitates to making this structure distinct. It is obtained by taking the average of all the factors in total relation matrix (T). It may help to reflect how one success factor influences other factors, and assists to filter out some negligible effects in the causal effect map. The causal-effect map of the main factors is shown in Fig. 3.

Based upon the prominence values, the importance of main factors in the implementation of RL practices in Indian manufacturing industries is given as GCF-SF-HROF-RF and EF (see Fig. 3). Further identified six main factors have been categorized into cause-effect groups. The cause-effect diagram provides valu- able insight to analyze the main factors in the implementation of reverse logistics in Indian manufacturing industries. The main factors – namely GCF, RF, and HROF – have been categorized into the cause group, and the other two main factors (namely SF and EF) are categorized into the effect group.

With respect to the differentmain factors, their position, and the relative importance in the system, experts distinguish the main factor which affects the decisions of the implementation of RL in Indian manufacturing industries greatly, and thus, improvements are made accordingly. Similarly, the DEMATEL calculations have been performed for sub factors within their respective main factors (Appendix A). The causal-effect map for the sub factors has also been formed as shown in Appendix B.

Table 2 Common success factors related to RL implementation.

S. No. Success factor Description Source

Regulatory factors (RF) 1 Government norms and support (RF1) Government directives and support act as very important factors for

industries to put RL in practice Kumar and Putnam, 2008; Hung Lau and Wang, 2009; Subramoniam et al., 2009; Ho et al., 2012; Rahman and Subramanian, 2012

2 Preferential tax policies (RF2) Favorable taxation policies can motivate industries to implement RL practices

Shaik and Abdul-Kader, 2012; Abdulrahman et al., 2014

3 Environmental management certifications (RF3)

Certification helps organizations to start and encourage environmentally friendly activities in their business activities, and generates consciousness among the employees

Knemeyer et al., 2002; Kumar and Putnam, 2008; Hung Lau and Wang, 2009; Chio et al., 2012

4 Extended producer responsibility (RF4) Manufacturers should be responsible enough to manage products at their end-of-life within India

Opinion received from the experts

5 Waste management practices (RF5) Wastemanagement practices is a big concern for industries to contribute for society and environment

Knemeyer et al., 2002

Global competitiveness factors (GCF) 6 Competition (GCF1) Adopting RL practices can tremendously improve an organizational

competitive image in the market Knemeyer et al., 2002; Subramoniam et al., 2009; Chio et al., 2012; Giannetti et al., 2013

7 Benchmarking (GCF2) Benchmarking the operations may significantly improve the RL adoption at industrial context

Opinion received from the experts

8 Globalization (GCF3) Globalization proves to be a thrust for industries in RL adoption Opinion received from the experts 9 Green image building (GCF4) RL has been recognized as an important step for industries to enhance their

green image Hsu and Hu, 2009; Hung Lau andWang, 2009; Subramoniam et al., 2009; Mangla et al., 2014a

10 Sustainability (GCF5) Implementation of RL practices helps to bring sustainability in the business Kumar and Putnam, 2008; Lee et al., 2010; Luthra et al., 2014b; Mangla et al., 2015b; Nikolaou et al., 2013; Subramanian et al., 2014

Economic factors (EF) 11 Reduced consumption of raw/virgin

material (EF1) RL offers a huge scope of value recovery from used products, so helps in reducing the raw/virgin material consumption

Seuring and Müller, 2008; Akdo�gan and Coşkun, 2012

12 Decreased waste generation (EF2) Adopting RL operations like recycling, reuse, remanufacturing results in the reduction of waste generation

Hung Lau and Wang, 2009; Pigosso et al., 2010; Akdo�gan and Coşkun, 2012

13 Financial opportunities (EF3) Financial opportunities in terms of the second hand market can be obtained through RL adoption

Rahman and Subramanian, 2012; Shaik and Abdul-Kader, 2012

HR and organizational factors (HROF) 14 Stakeholders' role and support (HROF1) Stakeholders such as investors, employees, management, etc. are

considered to be significant in making the decision to bring in the RL perspective within the business culture

Gonz�alez-Benito and Gonz�alez-Benito, 2006; Rahman and Subramanian, 2012; Shaik and Abdul-Kader, 2012

15 Experts involvement (HROF2) Experts involvement and knowledge can be valuable for the successful implementation of RL

Ho et al., 2012; Abdulrahman et al., 2014

16 Organization's policy and mission (HROF3)

Organization's policy, mission and vision are very crucial for the acceptance of the implementation of RL model

Dowlatshahi, 2005; Gonz�alez-Benito and Gonz�alez-Benito, 2006

17 Top management commitment and support (HROF4)

Top management commitment and support is very important for initiation and implementation of RL

Dowlatshahi, 2005; Abdulrahman et al., 2014

18 Employee expertise and involvement (HROF5)

RL implementation needs employee involvement; otherwise, its effective implementation could be very difficult

Ho et al., 2012; Abdulrahman et al., 2014

19 Customer environmental awareness (HROF6)

Customer environmental perception and knowledge is key to insist industries to adopt RL practices

Tsoulfas and Pappis, 2008; Rahman and Subramanian, 2012; Shaik and Abdul-Kader, 2012; Abdulrahman et al., 2014

Strategic factors (SF) 20 Integration and coordination (SF1) Integration and coordination among SC members may result in successful

implementation of RL Rahman and Subramanian, 2012; Lambert et al., 2011

21 Technology advancements (SF2) Adopting new processes and technology in RL initiatives will result in increased efficiency

Lambert et al., 2011; Shaik and Abdul-Kader, 2012

22 Management information system (SF3) It helps in bringing visibility within the system, thus, assisting on all levels of RL implementation

Lambert et al., 2011

23 Infrastructure (SF4) Infrastructure plays a major role in RL adoption Lambert et al., 2011 24 Understanding best practices (SF5) Understanding RL implementation best practices will be crucial at industrial

perspective Abdulrahman et al., 2014

25 Flexibility (SF6) Flexibility in operations, process and methods can help in adopting successful RL practices in business

Knemeyer et al., 2002; Bai and Sarkis, 2013; Nagarajan et al., 2013

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621614

6. Results and discussions

From Table 4, the main factors to implement RL practices can be arranged in relation to their relative importance or ranking as e Global competitiveness factors (GCF), Regulatory factors (RF), HR and organizational factors (HROF), Economic factors (EF), and Strategic factors (SF). The relative importance or ranking of the sub

factors has also been determined. Next, considering the DEMATEL results, the main factors GCF, RF, and HROF belong to the cause group, and the main factors EF and SF belong to the effect group. It clearly indicates that AHP based highly prioritized factors are the causal factors in accordance with the DEMATEL results. In addition, the importance order and causality mechanisms of the main factors and sub factors in efficient implementation of RL have also been

Fig. 2. AHP based hierarchical structure for the research.

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 615

recognized. Based on this, the present research offers several management science implications as follows.

The finding of this research work reveals that the Global competitiveness main factor (GCF) acquires the first rank, and consequently, it obtains the highest priority among other main factors (see Table 4). Regarding GCF, it is fitted to cause group factor (see Fig. 3), due to the positive value of (r e c) score (i.e. 0.875). It

Table 3 Pair wise evaluation matrix for main factors.

Factors RF GCF EF HROF SF

RF 1 1 3 2 3 GCF 1 1 5 3 5 EF 1/3 1/5 1 1/3 3 HROF ½ 1/3 3 1 3 SF 1/3 1/5 1/3 1/3 1

has a considerable significant influence on the other main factors. Therefore, it can be inferred that the GCF grouping is a crucial factor for industries in order to reduce the waste generation and emis- sions and to increase their environmental performances (Chio et al., 2012). Hence, it needs a great managerial commitment. Within this main factor, there are five sub factors, namely GCF1, GCF2, GCF3, GCF4, GCF5. These can be arranged in accordance with their

Table 4 Ranking of main factors in RL implementation.

Main factors Relative weights Ranks

GCF 0.3794 1 RF 0.285 2 HROF 0.1761 3 EF 0.0977 4 SF 0.0618 5

Table 5 Ranking of sub-factors in RL implementation.

Main factors Relative weights Sub factors Relative weights Relative ranking Global weights Global ranking

Regulatory factors (RF) 0.285 RF1 0.497 1 0.142 2 RF2 0.071 5 0.020 17 RF3 0.182 2 0.052 6 RF4 0.158 3 0.045 8 RF5 0.093 4 0.027 14

Global competitiveness factors (GCF) 0.379 GCF1 0.150 3 0.057 5 GCF2 0.124 4 0.047 7 GCF3 0.448 1 0.170 1 GCF4 0.208 2 0.079 3 GCF5 0.071 5 0.027 13

Economic factors (EF) 0.098 EF1 0.637 1 0.062 4 EF2 0.258 2 0.025 15 EF3 0.105 3 0.010 22

HR and organizational factors (HROF) 0.176 HROF1 0.200 2 0.035 10 HROF2 0.073 6 0.013 21 HROF3 0.162 4 0.029 12 HROF4 0.243 1 0.043 9 HROF5 0.135 5 0.024 16 HROF6 0.186 3 0.033 11

Strategic factors (SF) 0.062 SF1 0.217 3 0.013 20 SF2 0.278 1 0.017 18 SF3 0.224 2 0.014 19 SF4 0.119 4 0.007 23 SF5 0.113 5 0.007 24 SF6 0.048 6 0.003 25

Table 6 Average direct relation matrix (A) (Main factors).

0.00 2.67 2.33 2.00 2.33 2.33 0.00 2.33 2.00 2.33 0.67 1.33 0.00 1.67 2.00 1.33 2.00 2.33 0.00 2.33 1.67 1.67 1.67 2.00 0.00

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621616

priority, given as: - Globalization (GCF3), > Green image building (GCF4), > Competition (GCF1), >Benchmarking (GCF2), > Sustain- ability (GCF5). Globalization (GCF3), is highly ranked in global ranking column aswell (see Table 5), and demonstrated as themost important success factor for Indian industries to put into practice the RL initiative decisions. To have more insights into the results, the success factors GCF1, GCF2, GCF3 are categorized as cause group factors and GCF4, GCF5, are classified under effect group factors based on the (re c) values (see Appendix A). This grouping suggests that there is a critical need to regulate cause group factors, and consequently, the effect group factors can surely acknowledge the objectives of successful accomplishment of RL adoption across In- dian industries.

Regulatory main factors (RF) obtain the second highest priority in the list. The establishment of well-defined and environmental supportive regulating directives and guidelines is very significant for industries in adopting the RL initiatives at industrial standpoints (Jindal and Sangwan, 2011; Sharma et al., 2011). Further, in relation to this main factor, it finds its place among the cause group factor (Fig. 3), indicating that it may act as a major contributing factor to increase the success rate of RL adoption and implementation

Table 7 Normalized direct relation matrix (D) (Main factors).

0.00 0.29 0.26 0.22 0.26 0.26 0.00 0.26 0.22 0.26 0.07 0.15 0.00 0.18 0.22 0.15 0.22 0.26 0.00 0.26 0.18 0.18 0.18 0.22 0.00

among industries in Indian context. The five sub factors related to this main factor are from RF1 to RF5. The preference or relative importance order for these sub factors is given as Government norms and support (RF1), > Environmental management certifi- cations (RF3), > Extended producer responsibility (RF4), > Waste management practices (RF5), > Preferential tax policies (RF2). In addition to this, Government norms and support (RF1), is ranked secondly as per global ranking and proves to be a key factor in taking on RL aspects in business (Table 5). The success factors RF1, RF3, and RF4 found their places in the cause group (see Appendix A), which implies that they have significant influential impacts over the other factors found in the effect group (namely RF2, RF5). Moreover, all these factors play a significant role to the point of Indian industries where RL initiatives are still in infancy. Clearly, a systematic implementation of strategies and plans linked to these factors' completion may foster sustainable business developments among Indian industries.

HR and organizational factors (HROF) occupies third rank in the list. It finds its place among the cause group factor (Fig. 3), which implies that it is relatively important among all other main factors. Having proficient human resources, their expertise, and knowledge along with organizational capabilities in terms of employee involvement and their skills may resolve the difficulties relevant to RL adoption and provide an opportunity to undertake the accep- tance of RL's contemporary activities like reuse, recycling, or remanufacturing from business viewpoints (Sharma et al., 2011; Abdulrahman et al., 2014). Stakeholders such as investors and partners are pushing industries to accept RL related activities in

Table 8 Total relation and direct-indirect influence matrix (Main factors).

Factors RF GCF EF HROF SF R r þ c r e c RF 0.91 1.33 1.44 1.30 1.48 6.45 10.88 2.030 GCF 1.08 1.06 1.40 1.26 1.44 6.24 11.61 0.875 EF 0.66 0.83 0.79 0.88 1.00 4.15 10.16 �1.860 HROF 0.91 1.12 1.27 0.97 1.31 5.57 11.04 0.100 SF 0.87 1.02 1.13 1.06 1.01 5.08 11.31 �1.145 C 4.42 5.37 6.01 5.47 6.24 Threshold value ¼ 1.10

RF

GCF

EF

HROF

SF

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

10.00 10.20 10.40 10.60 10.80 11.00 11.20 11.40 11.60 11.80

Fig. 3. Causal effect map (Main factors).

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 617

supply chains (Chio et al., 2012). The relative ranking of six sub factors related to this main factor are - Top management commit- ment and support (HROF4), > Stakeholders' role and support (HROF1), > Customer environmental awareness (HROF6), > Orga- nization's policy and mission (HROF3), > expertise and involve- ment (HROF5), > Experts involvement (HROF2). Moreover, success factors HROF1- HROF4- HROF6 occurs in the cause group, which has a significant influence over the factors HROF2- HROF3- HROF5, which are in the effect group (see Appendix A).

Economic factors (EF) acquired the fourth importance level and play a crucial role in adopting effective green concepts in an in- dustrial context. Further, considering the causal effect map, it be- longs to effect group. It suggests that the various associated activities in implementation of RL practices have a tendency to in- fluence finance flow and resources (Chio et al., 2012). Under this consideration, it may be difficult for Indian industries to initiate and adopt RL practices in the initial stage of business, but at the later stage, it will offer a huge financial opportunity in terms of the reduction of raw material consumption, of significant cuts in waste generation, and of financial opportunities for used products in the new or secondary market, etc. Thus, the Indian industries may be financially benefitted and contribute more to their country's econ- omy. This main factor contains three sub factors and the priority order for them is listed as - Reduced consumption of raw/virgin material (EF1), > Decreased waste generation (EF2), > Financial opportunities (EF3). With regard to these three sub factors, EF1 and EF2 fall under the cause group, whereas EF3 comes under the effect group (see Appendix B). Thus, managers are suggested to consid- erably resolve the causal matters helpful in performance improve- ments in aspects of RL adoption and implementation.

Strategic factors (SF) hold the last place in priority list. It would be valuable for industries if they have strategic plans and visions associated with adoption and implementation of RL practices in their businesses. Understanding and analyzing Strategic factors is important to develop strengths into competitive advantages and to improve certain weaknesses related to technology, infrastructure, supply chain coordination and integration, and flexibility; such improvements will result in increased environmental, economical, and social performances of the Indian industries. There are six sub factors in strategic factors, and the preference order for them is highlighted as Technology advancements (SF2), > Management information system (SF3), > Integration and coordination (SF1), > Infrastructure (SF4), > Understanding best practices (SF5), > Operational flexibility (SF6). The success factors SF1, SF2, SF3 and SF4 found their places in the cause group (see Appendix B), which implies that they have significant influential impacts over the other factors occurring in the effect group, namely SF5 and SF6.

AHP results of main factors to implement RL practices in relation to their priority are given, in order, as GCF-RF-HROF-EF and SF. The DEMATTEL results of main factors to implement RL practices ac- cording to the prominence values are given as GCF-SF-HROF-RF and EF. From this, we can say that AHP and the prominence results are almost consistent. The combined results will help managers not only to prioritize the RL implementation success factors, but also to obtain their causal interactive relationships. This understanding may result in performance improvements in their industries, and it may help to ensure sustainable business developments.

6.1. Implications of research

The AHP and DEMATEL based model proposed in this work will enable Indian manufacturing company managers to understand different CSFs to implement RL practices in India. It would be crucial to know the relative importance and causal interactions of the various CSFs and the techniques for implementing RL adoption from industrial standpoints. This research work will certainly pre- pare them for the more efficient and effective implementation of RL practices in India. The findings obtained in this work will help managers and practitioners to improve the sustainability of the organizations in implementing RL practices of the industries. CSFs with higher priority demonstrate more of a tactical or operational orientation; on the other hand, those categorized as cause and ef- fect groups are more geared towards performance and result orientation. However, strategic results/desired effects can be ach- ieved by continuously improving cause group factors. This work may help RL practitioners/managers to manage these identified CSFs according to their AHP ranking priority and DEMATEL based prominence to achieve sustainability in the business.

From the results, Global competitiveness and Regulatory factors are highly prioritized factors and belong to cause group factors as well. In that way, the companies should contact and lobby the government and regulating authorities to express their concerns of the issue of RL implementation and its benefits in business. Gov- ernment and various regulating agencies support is much needed to adopt green and product recovery activities (Madaan and Mangla, 2015). To help companies, a well-designed and system- atic reverse logistics network is recommended to overcome the complexities in returning and recycling collected products for their reuse (Mangla et al., 2015a). In this sense, some motivational pro- grams and seminars/campaigns may be conducted to educate customers regarding products' reuse, recyclability, etc. In addition, some easily accessible collection stations may be opened to enhance the return and recovery of used products. Strict penalty and rewards systems may improve the recovery mechanism.

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621618

To end with, the proposed network model may provide some valuable guidelines to the supply chain analysts and management science professionals to develop their plan of action in terms of design of the short-term and the long-term, flexible decision stra- tegies for implementing RL on various business levels.

Factors RF1 RF2 RF3 RF4 RF5 r r þ c r e c RF1 0.53 1.03 0.82 0.95 0.83 4.17 6.76 1.58 RF2 0.45 0.54 0.55 0.65 0.54 2.73 6.77 �1.31 RF3 0.57 0.85 0.50 0.69 0.65 3.25 6.38 0.11 RF4 0.61 0.91 0.75 0.61 0.66 3.54 7.05 0.02 RF5 0.44 0.71 0.51 0.62 0.40 2.69 5.77 �0.39 c 2.59 4.04 3.14 3.52 3.08 Threshold value ¼ 0.65

Factors GCF1 GCF2 GCF3 GCF4 GCF5 R r þ c r e c GCF1 2.60 2.57 2.71 2.74 2.74 13.36 26.40 0.33 GCF2 2.76 2.34 2.69 2.66 2.67 13.13 25.07 1.18 GCF3 2.85 2.59 2.54 2.77 2.72 13.46 26.13 0.80 GCF4 2.41 2.22 2.34 2.18 2.37 11.53 24.24 �1.19 GCF5 2.42 2.23 2.38 2.36 2.19 11.58 24.27 �1.12 c 13.03 11.95 12.67 12.71 12.70 Threshold value ¼ 2.52

Factors EF1 EF2 EF3 r r þ c r e c EF1 1.03 1.25 1.66 3.93 7.02 0.84 EF2 1.15 0.80 1.45 3.40 6.19 0.60 EF3 0.92 0.76 0.84 2.52 6.47 �1.44 c 3.09 2.80 3.95 Threshold value ¼ 1.09

7. Conclusions, limitations, and scope for future work

Industries are constantly seeking ways to curb their negative impacts on the environment to ensure business sustainability. The implementation of reverse logistics (RL) practices has received major attention among developed countries; however, it needs a more in-depth examination in order to most effectively benefit a developing country such as India. Nevertheless, there are several critical factors linked to the implementation of RL practices. Hence, it is necessary to analyze these factors to increase the RL imple- mentation success rate. Therefore, from industrial viewpoints, the various CSFs related to the implementation of RL practices are evaluated in this study.

In this research work, an attempt has been made to evaluate the CSFs in RL implementation, by framing both short-term and long- term flexible decision strategies with AHP and DEMATEL methods. The AHP method helps to rank the factors (i.e. deter- mining of the priority) according to their relative importance. On the other hand, DEMATEL helps to establish interactive and or causal relationships between the factors, and classifies them into cause and effect groups.

The proposed AHP and DEMATEL based model is extended to the manufacturing industries in the Indian context. RL has been either already initiated or is in an early stage of adoption in the industries surveyed. A total of 25 common RL implementation CSFs have been selected, based on literature resources and the industry and field expert judgments.

The findings of this work shows that the Global competitiveness main factor (GCF) is highly prioritized, and thus, needs to be focused greatly in order to increase the effectiveness and efficiency of RL adoption in business. The relative priority or importance order of the remaining main factors through AHP analysis is given as Regulatory factors (RF) - HR and organizational factors (HROF), -Economic factors (EF) - Strategic factors (SF). The findings also indicate that Global competitiveness; Regulatory; HR and organi- zational main factors are classified under cause group, while Eco- nomic and Strategic main factors belong to effect group. The cause group factors are vital due to their direct impact on the overall system; therefore, it would be significant to focus on these group factors to expedite the overall performance. On the contrary, effect group factors tend to be easily affected by other factors (i.e. from the factors of cause group), and thus, make a significant contribu- tion towards achieving the desired goals (Mangla et al., 2015b. The results in terms of relative priorities and of interactive relationships for the sub factors are also derived.

This work has its own limitations, which can be taken as op- portunities for future research. The work carried out in this research is based on the methods of AHP and DEMATEL, and

Factors HROF1 HROF2 HROF3 HROF4

HROF1 0.70 1.07 1.19 1.09 HROF2 0.53 0.60 0.79 0.73 HROF3 0.75 0.97 0.89 0.98 HROF4 0.76 0.98 1.09 0.82 HROF5 0.64 0.90 0.96 0.86 HROF6 0.64 0.77 0.85 0.79 c 4.02 5.29 5.77 5.26

identifies 25 CSFs in the context of implementation of RL in Indian context. Some other CSFs have not been revealed and classified. For future studies, the hierarchical intertwined interactions and feed- back paths among recognized RL implementation CSFs can be analyzed by using other multi-criteria analysis methods like the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Analytic Network Process (ANP)methods, and other fuzzy or grey related MCDM approaches (Govindan et al., 2015a, 2016; Govindan and Chaudhuri, 2016; Xia et al., 2015). The proposed model may be applied to other sectors of industry, for example, service or construction that seeks to analyze the RL implementation performance at various business levels. It should be noted that the expert's opinion may vary with industry type and its priorities.

Appendix A

DEMATEL Calculations for Sub-factors within their respective Main factors.

Total relation and direct-indirect influence matrix (Regulatory factors).

Total relation and direct-indirect influence matrix (Global competitiveness factors).

Total relation and direct-indirect influence matrix (Economic factors).

Total relation and direct-indirect influence matrix (HR and organizational factors).

HROF5 HROF6 r r þ c r-c 0.97 0.90 5.92 9.94 1.89 0.67 0.55 3.87 9.16 �1.41 0.90 0.76 5.25 11.02 �0.51 0.95 0.71 5.30 10.56 0.03 0.66 0.65 4.68 9.49 �0.14 0.67 0.51 4.22 8.30 0.14 4.81 4.08 Threshold value ¼ 0.81

S.K. Mangla et al. / Journal of Cleaner Production 129 (2016) 608e621 619

Total relation and direct-indirect influence matrix (Strategic factors).

Factors SF1 SF2 SF3 SF4 SF5 SF6 r r þ c r-c SF1 0.84 1.01 1.09 0.98 1.12 1.07 6.11 11.84 0.37 SF2 1.09 0.98 1.23 1.09 1.27 1.26 6.91 12.85 0.97 SF3 1.03 1.04 0.95 1.02 1.20 1.14 6.38 12.62 0.15 SF4 0.98 0.99 1.00 0.83 1.16 1.06 6.03 11.79 0.26 SF5 0.82 0.89 0.90 0.87 0.85 0.95 5.27 12.00 �1.46 SF6 0.98 1.03 1.07 0.98 1.12 0.94 6.12 12.53 �0.29 c 5.74 5.94 6.24 5.77 6.73 6.41 Threshold value ¼ 1.02

Appendix B

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